Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Lead technical engagement for autonomous aerial systems in defense, integrating NVIDIA software and collaborating with industry leaders to optimize flight-ready solutions.

Design, build, and optimize GPU‑accelerated inference software and open‑source frameworks (vLLM, SGLang, FlashInfer). Improve performance and scale for LLMs and generative models across NVIDIA GPUs and edge SoCs using CUDA, CUTLASS, Triton, NCCL and profiling tools. Collaborate across teams to enable efficient model serving and deployment.

Lead deployment of NVIDIA GPU and networking solutions in customer data centers: design networks and compute/storage, debug cluster performance, run POCs, advise product roadmap, and support bring-up and customer engagements.

Lead and grow two engineering teams building NVIDIA's internal S3-compatible object storage and data movement tooling. Own roadmap execution, service reliability (SLOs, capacity, incident response), CI/CD and observability, recruiting and mentorship, and cross-team collaboration to ensure high-performance, scalable storage for AI workloads at exabyte scale.

Develop AI compiler solutions, optimize GPU programming, build training pipelines, collaborate on integrating AI policies, and conduct rigorous performance benchmarking.

Lead research and engineering for Nemotron post-training pipelines: develop synthetic data and agentic RL algorithms, build large-scale data and training infrastructure, collaborate on vendor data acquisition, optimize inference/deployment, and publish results.

As a Senior Software Architect for AI Networking, you'll design and implement AI networking technologies, optimize data processing in AI systems, and collaborate on cutting-edge solutions.

Manage a team developing accelerated image and data compression libraries. Define project goals, roadmap, and scope; ensure execution and delivery; elevate engineering practices; collaborate with internal and external customers and the open-source community while fostering an inclusive, innovative environment.

Lead product definition, execution, and go-to-market for AI physics models, libraries, and frameworks. Collaborate with engineering, developer relations, marketing, and sales; present to internal and external audiences; prioritize projects; translate technical capabilities into developer and customer value.

Design and build RL post-training infrastructure for AI, optimizing training-inference-rollout on distributed systems while ensuring fault tolerance and scalability.